Charts In Terms Research Articles

New adaptive CUSUM charts for process mean

In this article, we develop an unbiased estimator of the mean shift, and then use it to enhance the sensitivity of the existing adaptive CUSUM (ACUSUM) chart. Moreover, a new ACUSUM chart is proposed using the structure of the Crosier CUSUM chart and the new mean shift estimator. A comprehensive zero-state run length comparison is conducted between the proposed and existing ACUSUM charts in terms of the average run length measures, which shows that the former uniformly outperform the latter. A real dataset is also used to explain the implementation of existing and proposed ACUSUM charts.

Weighted adaptive multivariate CUSUM charts with variable sampling intervals

ABSTRACTThe adaptive multivariate CUSUM (AMCUSUM) chart has received considerable attention because of its superior sensitivity against a range of mean shift sizes than that of the conventional non-adaptive multivariate CUSUM (MCUSUM) chart. Recently, weighted AMCUSUM (WAMCUSUM) charts with a fixed sampling interval (FSI) have been proposed, called the WAMCUSUM-FSI charts, which provide more sensitivity than the AMCUSUM-FSI charts. In this paper, we extend this work and propose WAMCUSUM charts with variable sampling interval (VSI), named the WAMCUSUM-VSI charts, for efficiently monitoring the mean of a multivariate normally distributed process. The Monte Carlo simulation method is used to compute the average time to signal (ATS) and the adjusted ATS (AATS) profiles of the existing and proposed charts. It is found that the WAMCUSUM-VSI charts perform substantially and nearly uniformly better than the WAMCUSUM-FSI charts in terms of the ATS and AATS performance criterion. An example is given to explain the implementation of the WAMCUSUM charts with fixed and VSIs.

Computing dynamic across-wind response of tall buildings using artificial neural network

This paper presents the development of artificial neural network (ANN) model for predicting the dynamic across-wind response of tall building as per the provisions given in Indian Wind Code (IWC), IS 875 (Part-3):2015. IWC has given the procedure for estimating across-wind load response for limited aspect ratios of the building. For calculating the across-wind response not conforming to the ratios specified in the code, additional time-consuming and expensive studies are required. In order to minimise such tedious studies, an efficient computational technique is essential to determine wind response. ANN proves to be a promising alternative technique for analysing complex engineering problems. Thus, in this paper, using the best-fit model of ANN, predictions are made for the building configurations that are not included in IWC. The results are plotted as a series of charts in terms of base shear and the base bending moment. It is found that the predictions made using the trained and developed ANN models are in good agreement with the desired output.

A study on the variable sampling interval EWMA chart when the process parameters are unknown

The exponentially weighted moving average (EWMA) chart is well known in statistical process control for detecting small and moderate process mean shift. A variablesampling-interval (VSI) control scheme of the EWMA chart is able to enhance the speed of detection when the process is out-of-control as the intervals switch according to the value of the previous sample statistics. Generally, the performance of the VSI EWMA chart is investigated under the assumption of known process parameters. However, in reality, these parameters are usually unknown and are estimated from a historical Phase-I dataset. Hence, the performance of the VSI EWMA chart is significantly affected when the process parameters are unknown. This is due to estimation errors. In this paper, we investigate the effects of parameter estimation on the performance of the VSI EWMA chart in terms of the average time to signal and standard deviation of the time to signal. Suggestions on the required number of Phase-I samples and sample sizes in designing the VSI EWMA chart with unknown process parameters, are provided for practitioners’ fast implementation.

An adaptive multivariate EWMA chart

Abstract The adaptive multivariate EWMA (AMEWMA) and adaptive multivariate CUSUM (AMCUSUM) charts are recently proposed as they provide an overall good detection over a range of mean shift sizes than their non-adaptive conventional counterparts. In this paper, we propose an adaptive MEWMA (AMEWMA) chart for monitoring the infrequent changes in the mean of a multivariate normally distributed process. The idea is to consider an unbiased estimator of the mean shift using an MEWMA statistic, and then use it to adaptively update the smoothing constant of the MEWMA chart. By using Monte Carlo simulations, the run length characteristics of the AMEWMA chart are computed. The AMEWMA chart is comprehensively compared with the existing AMEWMA and AMCUSUM charts in terms of the average run length when detecting mean shifts in different ranges. It is found that the proposed chart is able to perform uniformly and substantially better than the existing charts. Both real and simulated datasets are considered to support the theory.

Safety of active catheters in MRI: Termination impedance versus RF-induced heating.

To investigate the effect of the termination impedance on the RF-induced heating of active catheters using analytical modeling. Interaction of an arbitrary electric (E) field and an isolated transmission line (TL) embedded in cascaded lossy media was analytically modeled. Termination impedances at the tip and the input sides were expressed as distinct parameters in the current and voltage distribution formulae that are obtained by solving the inhomogeneous wave equations using the Green's function approach. The tip specific absorption rate (SAR) was calculated for different E field configurations. The tip SAR was displayed on a color-coded Smith chart in terms of the normalized input reflection coefficient. Results of the analytical calculations were compared to transfer function (TF) measurements. An input impedance control unit that is integrated to the interface circuit was introduced. TFs from analytical model and measurements exhibited similar behaviors. Color-coded Smith charts shows that the analytical model and measured TF-based tip SAR depends strongly on the input reflection coefficient. Both for measured and analytical TFs, SAR can deviate up to 70% from the mean value for different input impedance values. This study shows that it is possible to change the RF-induced heating characteristics of an active catheter by adjusting the input impedance, and the presented analytical model is in good agreement with TF measurements.

A maximum adaptive exponentially weighted moving average control chart for monitoring process mean and variability

ABSTRACTThe maximum exponentially weighted moving average (MaxEWMA) chart is widely recognized as an efficient statistical process monitoring tool because of its ability to respond quickly against small-to-moderate shifts in the process parameters. In this article, we propose a maximum adaptive exponentially weighted moving average (MaxAEWMA) chart for simultaneously monitoring the mean and/or variance of a normally distributed process. Unlike the MaxEWMA chart, the MaxAEWMA chart provides an overall good performance for detecting a range of the mean and dispersion shift sizes rather than a single value. The run length characteristics of the MaxAEWMA chart are computed using extensive Monte Carlo simulations. The MaxAEWMA chart is comprehensively compared with the MaxEWMA chart in terms of the average and standard deviation of the run length. It is found that the MaxAEWMA chart performs substantially and uniformly better than the MaxEWMA chart. An example is given to explain the implementation of the MaxEWMA and MaxAEWMA charts.

A mixed nonparametric control chart for efficient process monitoring

In this paper, we propose a nonparametric mixed control chart using exponentially weighted moving average and cumulative sum (statistics for efficient monitoring of quality characteristic). The proposed control chart, namely NPMEC chart, proceeds in two steps. The necessary constants required in the construction of the proposed mixed chart are determined. The performance evaluation of the proposed chart in terms of run length characteristic is also considered. The comparison of this new mixed chart with the existing charts is also done here. The comparison reveals that the NPMEC chart performs efficiently for detecting a moderate to larger shift and almost reacts similarly or a bit late towards detecting the smaller shifts as compared to its counterparts.

A New Control Chart for Monitoring the Process Mean Using Successive Sampling and Multiple Dependent State Repetitive Sampling

In this paper, a new control chart scheme has been developed for monitoring the production process mean using successive sampling over two occasions. The proposed chart reduces to three different existing control charts under different assumptions and is compared with these three existing control charts for monitoring the process average. It has been observed that the proposed control chart performs better than the other existing control charts in terms of average run length (ARL). A simulation study using an artificial data set was included for demonstrating the process shift detection power of the proposed control chart.

Robust bootstrap control charts for percentiles based on model selection approaches

Abstract This paper presents two model selection approaches, namely the random data-driven approach and the weighted modeling approach, to construct robust bootstrap control charts for process monitoring of percentiles of the shape-scale class of distributions under model uncertainty. The generalized exponential, lognormal and Weibull distributions are considered as candidate distributions to establish the proposed process control procedures. Monte Carlo simulations are conducted with various combinations of the percentiles, false-alarm rates and sample sizes to evaluate the performance of the proposed robust bootstrap control charts in terms of the average run lengths. Simulation results exhibit that the two proposed robust model selection approaches perform well when the underlying distribution of the quality characteristic is unknown. Finally, the proposed process monitoring procedures are applied to two data sets for illustration.

A Nonparametric Phase I Control Chart for Monitoring the Process Variability with Individual Observations Based on Empirical Likelihood Ratio

Among the statistical process control (SPC) techniques, the control chart has been proven to be effective in process monitoring. The Shewhart chart is one of the most commonly used control charts for monitoring the process mean and variability based on the assumption that the distribution of the quality characteristic is normal. However, in practice, many quality characteristics are not normally distributed. Most of the existing nonparametric control charts are designed for Phase II monitoring. Little has been done in developing the nonparametric Phase I control charts especially for individual observations. In this work, we propose a new nonparametric Phase I control chart for monitoring the scale parameter based on the empirical likelihood ratio test. The simulation results show that the proposed chart is more effective than the existing charts in terms of signal probability. A real example is used to demonstrate how the proposed chart can be applied in practice.

VSSI median control chart with estimated parameters and measurement errors

AbstractAn adaptive control chart called Shewhart chart with variable sample size and sampling interval (VSSI) is quicker than Shewhart chart, chart with variable sample size (VSS), and chart with variable sampling interval (VSI) in detecting the mean shifts of a normal process. In practice, the effects of measurement errors on control charts should be included. In this study, we present an VSSI median control chart with estimated parameters in the presence of measurement errors for a normal process. The average time to signal (ATS) is computed by using the Markov chain approach. The results show that the VSSI median control chart performs better than the Shewhart median, VSS median, and VSI median control charts in terms of ATS. The design parameters of the proposed chart are provided. Two examples are used to illustrate the application of the proposed control chart.

Steady‐state ARL analysis of ARL‐unbiased EWMA‐RZ control chart monitoring the ratio of two normal variables

AbstractVery recently, control charts for monitoring the ratio of 2 normal variables have been investigated in statistical process control. In the two‐sided case, however, these control charts tend to be average run length (ARL) biased, in the sense that some out‐of‐control ARL values are larger than the in‐control ARL. This paper proposes an ARL‐unbiased EWMA control chart for monitoring of this kind of ratio with each subgroup consisting of n⩾1 sample units. Also, to study the long‐term properties of ARL‐unbiased EWMA‐RZ control chart, we investigate the steady‐state ARL. Several tables and figures are given to show the statistical properties of the proposed control charts. The comparison results show that the proposed ARL‐unbiased chart outperforms other two‐sided control charts in terms of the zero‐state and steady‐state ARL. An example illustrates the use of this chart on a real quality control problem from the food industry.

New control charts for monitoring the Weibull percentiles under complete data and Type‐II censoring

AbstractIn this paper, we propose 3 new control charts for monitoring the lower Weibull percentiles under complete data and Type‐II censoring. In transforming the Weibull distribution to the smallest extreme value distribution, Pascaul et al (2017) presented an exponentially weighted moving average (EWMA) control chart, hereafter referred to as EWMA‐SEV‐Q, based on a pivotal quantity conditioned on ancillary statistics. We extended their concept to construct a cumulative sum (CUSUM) control chart denoted by CUSUM‐SEV‐Q. We provide more insights of the statistical properties of the monitoring statistic. Additionally, in transforming a Weibull distribution to a standard normal distribution, we propose EWMA and CUSUM control charts, denoted as EWMA‐YP and CUSUM‐YP, respectively, based on a pivotal quantity for monitoring the Weibull percentiles with complete data. With complete data, the EWMA‐YP and CUSUM‐YP control charts perform better than the EWMA‐SEV‐Q and CUSUM‐SEV‐Q control charts in terms of average run length. In Type‐II censoring, the EWMA‐SEV‐Q chart is slightly better than the CUSUM‐SEV‐Q chart in terms of average run length. Two numerical examples are used to illustrate the applications of the proposed control charts.

Time truncated attribute control chart for the Weibull distribution using multiple dependent state sampling

ABSTRACTIn this article, an attribute control chart is proposed for time truncated tests using the Weibull distribution. The design of proposed control chart is presented using the multiple dependent state (MDS) sampling. The control chart coefficients are determined for various specified average run length. The efficiency of the proposed control chart is elaborated with the help of a simulation data and a real data. The proposed control chart perform better than the existing control chart in terms of average run length.

A HEWMA-CUSUM control chart for the Weibull distribution

ABSTRACTThe Weibull distribution is one of the most popular distributions for lifetime modeling. However, there has not been much research on control charts for a Weibull distribution. Shewhart control is known to be inefficient to detect a small shift in the process, while exponentially weighted moving average (EWMA) and cumulative sum control chart (CUSUM) charts have the ability to detect small changes in the process. To enhance the performance of a control chart for a Weibull distribution, we introduce a new control chart based on hybrid EWMA and CUSUM statistic, called the HEWMA-CUSUM chart. The performance of the proposed chart is compared with the existing chart in terms of the average run length (ARL). The proposed chart is found to be more sensitive than the existing chart in ARL. A simulation study is provided for illustration purposes. A real data is also applied to the proposed chart for practical use.

I-O Psychology and Human Resource Development: Yes, and . . .

It is encouraging to see the diversity of sources cited in the top introductory textbooks in industrial-organizational (I-O) psychology. It is not surprising that the Journal of Applied Psychology (JAP) and Personnel Psychology are off the charts in terms of the two most-cited sources in these six textbooks. However, I would not have expected that in these books, “77% of the top-cited articles were published in cross-disciplinary journals” (Aguinis et al., 2017, p. 507). I wish to build on Aguinis et al. (2017), with a focus on the relationship between I-O psychology and human resource development (HRD). I am the author of an HRD textbook (Werner, 2017a). The journal I co-edit, Human Resource Development Quarterly, is one of those “cross-disciplinary journals” on Aguinis et al.’s list of most-cited sources (56th out of 110, 40th in academic journals), with 16 citations. Finally, my article on legal issues in performance appraisal was cited in five of the six textbooks (Werner & Bolino, 1997).

A new Markov chain approach for the economic statistical design of the VSS T2 control chart

ABSTRACTAn economic statistical design model for a T2 chart which uses a variable sample size (VSS) feature is developed in this article. This study mainly differs from the others conducted in the field. In that a new approach is offered to achieve closed form of some statistical criteria. In other words, the proposed formulas can be considered as a better alternative approach in designing the VSS control charts in terms of simplicity and yet providing the users with better optimal solutions.

Evaluation of an np control chart under truncated life test using quick switching sampling system

In this paper, we propose a new attribute np control chart for monitoring Weibull distributed mean life of a product using a quick switching sampling system. The proposed control chart consists of two pairs of control limits, namely normal and tightened control limits. The optimal parameters of the proposed control chart, such as coefficients of control limits and experiment termination ratio, are determined so that the average run length (ARL) is close to the target in-control ARL. The ARL is calculated for various shift constants for the corresponding determined parameters. The performance of the proposed control chart is evaluated and compared with other existing charts in terms of ARL.

An exponentially weighted moving average chart based on likelihood‐ratio test for monitoring Weibull mean and variance with subgroups

AbstractMonitoring changes in the Weibull mean and variance simultaneously is of interest in quality control. The mean and variance of a Weibull process are determined by its shape and scale parameters. Most studies are focused on monitoring the Weibull scale parameter with fixed shape parameter or the Weibull shape parameter with fixed scale parameter. In this paper, we propose an exponentially weighted moving average chart based on the likelihood‐ratio test and an inverse error function called ELR chart to monitor changes in the Weibull mean and variance simultaneously. The simulation approach is used to derive the average run length. We compare our proposed chart with other existing control charts for 3 cases, including scale parameter changes with fixed shape parameter, shape parameter changes with fixed scale parameter, and both parameters changes. The results show that the ELR chart outperforms the other control charts in terms of average run length in most cases. Two numerical examples are used to illustrate the applications of the proposed control chart.